Learning with Misattribution of Reference Dependence

Abstract

We examine errors in learning that arise when an agent who suffers attribution bias fails to account for her reference-dependent utility. Such an agent neglects how the sensation of elation or disappointment relative to expectations contributes to her overall utility, and wrongly attributes this component of her utility to the intrinsic value of an outcome. In a sequential-learning environment, this form of misattribution generates contrast effects in evaluations and induces a recency bias—the misattributor’s beliefs over-weight recent experiences and under-weight earlier ones. In the long-run, a loss-averse misattributor will grow unduly pessimistic and undervalue prospects in proportion to their variability. Both the short and long-run properties of beliefs under misattribution suggest a tendency to abandon worthwhile prospects when learning from experience. We additionally show how misattribution introduces incentives for familiar forms of expectations management.